Retrospective Illumination Correction of Retinal Images

A method for correction of nonhomogenous illumination based on optimization of parameters of B-spline shading model with respect to Shannon's entropy is presented. The evaluation of Shannon's entropy is based on Parzen windowing method (Mangin, 2000) with the spline-based shading model. This allows us to express the derivatives of the entropy criterion analytically, which enables efficient use of gradient-based optimization algorithms. Seven different gradient- and nongradient-based optimization algorithms were initially tested on a set of 40 simulated retinal images, generated by a model of the respective image acquisition system. Among the tested optimizers, the gradient-based optimizer with varying step has shown to have the fastest convergence while providing the best precision. The final algorithm proved to be able of suppressing approximately 70% of the artificially introduced non-homogenous illumination. To assess the practical utility of the method, it was qualitatively tested on a set of 336 real retinal images; it proved the ability of eliminating the illumination inhomogeneity substantially in most of cases. The application field of this method is especially in preprocessing of retinal images, as preparation for reliable segmentation or registration

Fusion based analysis of ophthalmologic image data

summary:The paper presents an overview of image analysis activities of the Brno DAR group in the medical application area of retinal imaging. Particularly, illumination correction and SNR enhancement by registered averaging as preprocessing steps are briefly described; further mono- and multimodal registration methods developed for specific types of ophthalmological images, and methods for segmentation of optical disc, retinal vessel tree and autofluorescence areas are presented. Finally, the designed methods for neural fibre layer detection and evaluation on retinal images, utilising different combined texture analysis approaches and several types of classifiers, are shown. The results in all the areas are shortly commented on at the respective sections. In order to emphasise methodological aspects, the methods and results are ordered according to consequential phases of processing rather then divided according to individual medical applications

Illumination Correction on Biomedical Images

RF-Inhomogeneity Correction (aka bias) artifact is an important research field in Magnetic Resonance Imaging (MRI). Bias corrupts MR images altering their illumination even though they are acquired with the most recent scanners. Homomorphic Unsharp Masking (HUM) is a filtering technique aimed at correcting illumination inhomogeneity, but it produces a halo around the edges as a side effect. In this paper a novel correction scheme based on HUM is proposed to correct the artifact mentioned above without introducing the halo. A wide experimentation has been performed on MR images. The method has been tuned and evaluated using the simulated Brainweb image database. In this framework, the approach has been compared successfully against the Guillemaud filter and the SPM2 method. Moreover, the method has been successfully applied on several real MR images of the brain (0.18 T, 1.5 T and 7 T). The description of the overall technique is reported along with the experimental results that show its effectiveness in different anatomical regions and its ability to compensate both underexposed and overexposed areas. Our approach is also effective on non-radiological images, like retinal ones

Uniformisation de l'éclairage en imagerie rétinienne : Application pour les images d'autofluorescence du fond d'œil

Texte intégral accessible en ligne à http://documents.irevues.inist.fr/bitstream/handle/2042/28924/rashidchaudhry_313.pdfInternational audienceDans cet article nous proposons une méthode originale pour corriger l'éclairage inhomogène des images de la rétine. Notre méthode utilise la convolution de l'image par un filtre gaussien de taille appropriée définie automatiquement par l'analyse de la courbe d'entropie de l'image corrigée. La luminosité moyenne de l'image originale est conservée ainsi que les structures et les lésions.Title: Uniformization of lighting in retinal imaging : Application to autofluorescence images of fundus oculiAbstract: In this paper, we propose an original method for the correction of inhomogeneous illumination in the retinal images. Our method uses the convolution of an image by Gaussian filter of an appropriate size. The size of the Gaussian filter is automatically determined by the analysis of entropy curve of the corrected image. The original brightness of the image is preserved as well as the retinal structural and lesions

NON-INVASIVE IMAGE DENOISING AND CONTRAST ENHANCEMENT TECHNIQUES FOR RETINAL FUNDUS IMAGES

The analysis of retinal vasculature in digital fundus images is important for diagnosing eye related diseases. However, digital colour fundus images suffer from low and varied contrast, and are also affected by noise, requiring the use of fundus angiogram modality. The Fundus Fluorescein Angiogram (FFA) modality gives 5 to 6 time’s higher contrast. However, FFA is an invasive method that requires contrast agents to be injected and this can lead other physiological problems. A reported digital image enhancement technique named RETICA that combines Retinex and ICA (Independent Component Analysis) techniques, reduces varied contrast, and enhances the low contrast blood vessels of model fundus images

NON-INVASIVE IMAGE ENHANCEMENT OF COLOUR RETINAL FUNDUS IMAGES FOR A COMPUTERISED DIABETIC RETINOPATHY MONITORING AND GRADING SYSTEM

Diabetic Retinopathy (DR) is a sight threatening complication due to diabetes mellitus affecting the retina. The pathologies of DR can be monitored by analysing colour fundus images. However, the low and varied contrast between retinal vessels and the background in colour fundus images remains an impediment to visual analysis in particular in analysing tiny retinal vessels and capillary networks. To circumvent this problem, fundus fluorescein angiography (FF A) that improves the image contrast is used. Unfortunately, it is an invasive procedure (injection of contrast dyes) that leads to other physiological problems and in the worst case may cause death. The objective of this research is to develop a non-invasive digital Image enhancement scheme that can overcome the problem of the varied and low contrast colour fundus images in order that the contrast produced is comparable to the invasive fluorescein method, and without introducing noise or artefacts. The developed image enhancement algorithm (called RETICA) is incorporated into a newly developed computerised DR system (called RETINO) that is capable to monitor and grade DR severity using colour fundus images. RETINO grades DR severity into five stages, namely No DR, Mild Non Proliferative DR (NPDR), Moderate NPDR, Severe NPDR and Proliferative DR (PDR) by enhancing the quality of digital colour fundus image using RETICA in the macular region and analysing the enlargement of the foveal avascular zone (F AZ), a region devoid of retinal vessels in the macular region. The importance of this research is to improve image quality in order to increase the accuracy, sensitivity and specificity of DR diagnosis, and to enable DR grading through either direct observation or computer assisted diagnosis system

Retinal Pigment Epithelial and Outer Retinal Atrophy in Age-Related Macular Degeneration: Correlation with Macular Function

The purpose of this study was to investigate the relationship between the retinal pigment epithelium (RPE) and outer retina changes, expressed in terms of sub-RPE illumination (SRI) on optical-coherence tomography (OCT), and central retinal function, measured by visual acuity and focal electroretinogram (fERG), in patients with non-exudative age-related macular degeneration (neAMD). In this retrospective study, 29 eyes of 29 patients affected by early (24.14%), intermediate (41.38%), and advanced (34.48%) neAMD were evaluated. All enrolled eyes were studied with OCT to measure the total area of SRI, by using an automated standardized algorithm. Visual acuity and fERG were assessed. The area of SRI was negatively correlated with fERG amplitude (r <= -0.4, p <= 0.02) and best-corrected visual acuity (BCVA) (r <= 0.4, p <= 0.04). Our results indicate that the severity of retinal pigment epithelium and outer retina atrophy (RORA), indirectly quantified through the detection of SRI areas by commercial OCT algorithms, is correlated with central retinal dysfunction, as determined by visual acuity and fERG, supporting the combined use of structural exams and functional tests as valid tools to detect the extent of RPE and photoreceptors' disruption

Design of a Novel Low Cost Point of Care Tampon (POCkeT) Colposcope for Use in Resource Limited Settings

Introduction: Current guidelines by WHO for cervical cancer screening in low- and middle-income countries involves visual inspection with acetic acid (VIA) of the cervix, followed by treatment during the same visit or a subsequent visit with cryotherapy if a suspicious lesion is found. Implementation of these guidelines is hampered by a lack of: trained health workers, reliable technology, and access to screening facilities. A low cost ultra-portable Point of Care Tampon based digital colposcope (POCkeT Colposcope) for use at the community level setting, which has the unique form factor of a tampon, can be inserted into the vagina to capture images of the cervix, which are on par with that of a state of the art colposcope, at a fraction of the cost. A repository of images to be compiled that can be used to empower front line workers to become more effective through virtual dynamic training. By task shifting to the community setting, this technology could potentially provide significantly greater cervical screening access to where the most vulnerable women live. The POCkeT Colposcope’s concentric LED ring provides comparable white and green field illumination at a fraction of the electrical power required in commercial colposcopes. Evaluation with standard optical imaging targets to assess the POCkeT Colposcope against the state of the art digital colposcope and other VIAM technologies. Results: Our POCkeT Colposcope has comparable resolving power, color reproduction accuracy, minimal lens distortion, and illumination when compared to commercially available colposcopes. In vitro and pilot in vivo imaging results are promising with our POCkeT Colposcope capturing comparable quality images to commercial systems. Methods: Rapid 3D printing, consumer grade light sources, and cameras were used to construct the TVDC. The TVDC’s concentric LED ring provides comparable white and green field illumination at a fraction of the electrical power required in commercial colposcopes, and crossed polarizers provide a reduction in glare. Evaluation was performed using standard optical imaging targets to assess the TVDC against the state of the art digital colposcope and other VIA technologies. Results: Our TVDC has comparable resolving power, color reproduction accuracy, minimal lens distortion, and illumination when compared to commercially available colposcopes. In vitro and pilot in vivo imaging results are promising with our TVDC capturing images of comparable quality to commercial systems. Conclusion: The TVDC is capable of capturing images suitable for cervical lesion analysis. Our portable low cost system will be useful for increasing access to cervical cancer screening and diagnostics in resource-limited settings by providing a more readily portable and easy to use device for medical personnel.The image data and support information that is published in the article "Design of a Novel Low Cost Trans-Vaginal Digital Colposcope for use in Resource Limited Settings" are available at: http://dukespace.lib.duke.edu/dspace/handle/10161/8357.National Institutes of Health (US) 5R21CA162747-0

Deep learning network to correct axial and coronal eye motion in 3D OCT retinal imaging

Optical Coherence Tomography (OCT) is one of the most important retinal imaging technique. However, involuntary motion artifacts still pose a major challenge in OCT imaging that compromises the quality of downstream analysis, such as retinal layer segmentation and OCT Angiography. We propose deep learning based neural networks to correct axial and coronal motion artifacts in OCT based on a single volumetric scan. The proposed method consists of two fully-convolutional neural networks that predict Z and X dimensional displacement maps sequentially in two stages. The experimental result shows that the proposed method can effectively correct motion artifacts and achieve smaller error than other methods. Specifically, the method can recover the overall curvature of the retina, and can be generalized well to various diseases and resolutions